Contents 
import geopandas as gpd
from libpysal import graph
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import LogNorm
from matplotlib_scalebar.scalebar import ScaleBar
import pandas as pd
import contextily as cx

signatures = gpd.read_parquet("../../urbangrammar_samba/spatial_signatures/signatures/signatures_combined_levels_orig.pq")

signatures = gpd.read_file("spatial_signatures_GB.gpkg")

signatures
id code type geometry
0 0_COA COA Countryside agriculture POLYGON ((62220.000 798500.000, 62110.000 7985...
1 1_COA COA Countryside agriculture POLYGON ((63507.682 796515.169, 63471.097 7965...
2 2_COA COA Countryside agriculture POLYGON ((65953.174 802246.172, 65950.620 8022...
3 3_COA COA Countryside agriculture POLYGON ((67297.740 803435.800, 67220.289 8034...
4 4_COA COA Countryside agriculture POLYGON ((75760.000 852670.000, 75700.000 8527...
... ... ... ... ...
96699 96699_OUT OUT outlier POLYGON ((323321.005 463795.416, 323319.842 46...
96700 96700_OUT OUT outlier POLYGON ((325929.840 1008792.061, 325927.377 1...
96701 96701_OUT OUT outlier POLYGON ((337804.770 1013422.583, 337800.122 1...
96702 96702_OUT OUT outlier POLYGON ((422304.270 1147826.990, 422296.000 1...
96703 96703_OUT OUT outlier POLYGON ((525396.260 439215.480, 525360.920 43...

96704 rows × 4 columns

signatures.code.unique()

array(['COA', 'ACS', 'OPS', 'WIC', 'WAL', 'GRQ', 'URB', 'DIS', 'DRN',
       'CRN', 'DUN', 'LOU', 'DIU', 'REU', 'OUT', 'MEU', 'HDU'],
      dtype=object)

# drop an outlier island
signatures = signatures.drop(96691)

signatures_urban = signatures[~signatures.code.isin(["OUT", "COA", "URB", "WIC"])].copy()

signatures_urban
id code type geometry
10882 10882_ACS ACS Accessible suburbia POLYGON ((143345.668 932691.413, 143345.418 93...
10883 10883_ACS ACS Accessible suburbia POLYGON ((151573.910 30042.750, 151571.029 300...
10884 10884_ACS ACS Accessible suburbia POLYGON ((151697.494 29760.709, 151650.000 297...
10885 10885_ACS ACS Accessible suburbia POLYGON ((162909.562 26425.073, 162911.932 264...
10886 10886_ACS ACS Accessible suburbia POLYGON ((164091.320 40526.696, 164093.601 405...
... ... ... ... ...
96687 96687_HDU HDU Hyper concentrated urbanity POLYGON ((528602.732 181135.450, 528572.630 18...
96688 96688_HDU HDU Hyper concentrated urbanity POLYGON ((528643.907 181217.443, 528638.400 18...
96689 96689_HDU HDU Hyper concentrated urbanity POLYGON ((528734.582 181316.822, 528732.783 18...
96690 96690_HDU HDU Hyper concentrated urbanity POLYGON ((530336.433 181039.755, 530336.117 18...
96691 96691_HDU HDU Hyper concentrated urbanity POLYGON ((581275.000 184964.000, 581431.000 18...

68957 rows × 4 columns

%%time
contiguity = graph.Graph.build_fuzzy_contiguity(signatures_urban, buffer=1)

CPU times: user 2min 16s, sys: 2.21 s, total: 2min 18s
Wall time: 2min 18s

%%time
signatures_urban["settlement"] = contiguity.component_labels

CPU times: user 16.2 ms, sys: 0 ns, total: 16.2 ms
Wall time: 15.6 ms

types = {
    "0_0": "Countryside agriculture",
    "1_0": "Accessible suburbia",
    "3_0": "Open sprawl",
    "4_0": "Wild countryside",
    "5_0": "Warehouse/Park land",
    "6_0": "Gridded residential quarters",
    "7_0": "Urban buffer",
    "8_0": "Disconnected suburbia",
    "2_0": "Dense residential neighbourhoods",
    "2_1": "Connected residential neighbourhoods",
    "2_2": "Dense urban neighbourhoods",
    "9_0": "Local urbanity",
    "9_1": "Concentrated urbanity",
    "9_2": "Regional urbanity",
    "9_4": "Metropolitan urbanity",
    "9_5": "Hyper concentrated urbanity",
}

signatures_urban["signature_type"] = signatures_urban["signature_type"].map(types)

signatures_urban.settlement.max()

9436

signatures_urban["area"] = signatures_urban.area

grouper = signatures_urban.groupby(["settlement", "type"])
areas = grouper["area"].sum().unstack().fillna(0)

areas
type Accessible suburbia Concentrated urbanity Connected residential neighbourhoods Dense residential neighbourhoods Dense urban neighbourhoods Disconnected suburbia Gridded residential quarters Hyper concentrated urbanity Local urbanity Metropolitan urbanity Open sprawl Regional urbanity Warehouse/Park land
settlement
0 98432.753668 0.0 658585.378043 82103.007472 16858.821454 3.867427e+04 194436.499973 0.0 0.0 0.000000 2.443473e+05 0.0 392625.177314
1 39159.397355 0.0 0.000000 0.000000 0.000000 4.303130e+03 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000
2 1714.392324 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 0.000000 7.653142e+02 0.0 0.000000
3 194126.224307 0.0 0.000000 0.000000 0.000000 1.116033e+06 245226.403996 0.0 0.0 0.000000 1.727041e+06 0.0 0.000000
4 98.243984 0.0 14951.756016 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ...
9432 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 3069.922515 0.000000e+00 0.0 0.000000
9433 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 6332.353514 0.000000e+00 0.0 0.000000
9434 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 563.416847 0.000000e+00 0.0 0.000000
9435 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 0.0 0.0 6822.794200 0.000000e+00 0.0 0.000000
9436 0.000000 0.0 0.000000 0.000000 0.000000 0.000000e+00 0.000000 987539.5 0.0 0.000000 0.000000e+00 0.0 0.000000

9437 rows × 13 columns

order = [
       'Warehouse/Park land', 'Open sprawl', 'Disconnected suburbia',
       'Accessible suburbia', 'Connected residential neighbourhoods',
       'Dense residential neighbourhoods', 'Gridded residential quarters',
       'Dense urban neighbourhoods', 'Local urbanity', 'Regional urbanity',
       'Metropolitan urbanity', 'Concentrated urbanity',
       'Hyper concentrated urbanity'
    ]
order.reverse()

areas_sorted = areas.sort_values(order, ascending=False)[order]

plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.iloc[:200].T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='type'>
../_images/settlment_delin_17_1.png 
areas["total"] = areas.sum(axis=1)
areas.sort_values("total")
type Accessible suburbia Concentrated urbanity Connected residential neighbourhoods Dense residential neighbourhoods Dense urban neighbourhoods Disconnected suburbia Gridded residential quarters Hyper concentrated urbanity Local urbanity Metropolitan urbanity Open sprawl Regional urbanity Warehouse/Park land total
settlement
3981 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.598476e-04 0.000000e+00 0.000000e+00 1.598476e-04
5126 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 5.270652e-03 0.000000e+00 0.000000e+00 5.270652e-03
6706 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.706335e-02 0.000000e+00 0.000000e+00 1.706335e-02
8593 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 5.482056e-01 5.482056e-01
3588 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 5.982074e-01 0.000000e+00 0.000000e+00 5.982074e-01
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
984 8.444854e+07 0.000000e+00 1.337555e+07 3.137510e+07 2.479331e+07 1.552982e+07 2.332107e+06 0.000000e+00 6.522618e+06 1.001744e+05 2.786930e+08 1.905346e+06 1.668628e+07 4.757619e+08
1006 1.173756e+07 0.000000e+00 3.097936e+07 5.928168e+07 3.574174e+07 1.610781e+07 3.138634e+06 0.000000e+00 8.388703e+06 0.000000e+00 2.054715e+08 1.589767e+06 2.301844e+08 6.026212e+08
876 6.251046e+07 0.000000e+00 4.149977e+07 4.842811e+07 2.758493e+07 5.417000e+07 1.721331e+05 0.000000e+00 5.745378e+06 1.075760e+06 1.447718e+08 2.806010e+06 3.386026e+08 7.273669e+08
407 1.885914e+08 0.000000e+00 8.382661e+07 1.443043e+08 5.427237e+07 3.735568e+07 3.914361e+07 0.000000e+00 7.917305e+06 5.156702e+05 5.668694e+08 3.950296e+06 2.810701e+08 1.407817e+09
1845 2.132323e+08 7.884144e+06 1.364017e+08 2.235081e+08 2.201192e+08 8.389185e+07 6.699935e+06 1.306332e+06 1.155779e+08 1.321113e+07 2.989552e+08 3.960246e+07 3.819127e+08 1.742303e+09

9437 rows × 14 columns

areas["total"][areas["total"]<areas["total"].quantile(.75)].plot.hist(bins=150)

<Axes: ylabel='Frequency'>
../_images/settlment_delin_19_1.png 
areas["total"].quantile(.25)

534.3932255090165

areas["total"].median()

1345.9874175296875

plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='type'>
../_images/settlment_delin_22_1.png 
placenames = gpd.read_parquet("place.parquet")

%%time
joined = placenames.sjoin(signatures_urban[["geometry", "settlement"]])

CPU times: user 3.14 s, sys: 0 ns, total: 3.14 s
Wall time: 3.14 s

signatures_urban
id code type geometry settlement area
10882 10882_ACS ACS Accessible suburbia POLYGON ((143345.668 932691.413, 143345.418 93... 0 9.843275e+04
10883 10883_ACS ACS Accessible suburbia POLYGON ((151573.910 30042.750, 151571.029 300... 1 2.597607e+03
10884 10884_ACS ACS Accessible suburbia POLYGON ((151697.494 29760.709, 151650.000 297... 1 3.656179e+04
10885 10885_ACS ACS Accessible suburbia POLYGON ((162909.562 26425.073, 162911.932 264... 2 1.714392e+03
10886 10886_ACS ACS Accessible suburbia POLYGON ((164091.320 40526.696, 164093.601 405... 3 1.941262e+05
... ... ... ... ... ... ...
96687 96687_HDU HDU Hyper concentrated urbanity POLYGON ((528602.732 181135.450, 528572.630 18... 1845 1.797907e+03
96688 96688_HDU HDU Hyper concentrated urbanity POLYGON ((528643.907 181217.443, 528638.400 18... 1845 1.271663e+06
96689 96689_HDU HDU Hyper concentrated urbanity POLYGON ((528734.582 181316.822, 528732.783 18... 1845 1.872188e+03
96690 96690_HDU HDU Hyper concentrated urbanity POLYGON ((530336.433 181039.755, 530336.117 18... 1845 2.767724e+03
96691 96691_HDU HDU Hyper concentrated urbanity POLYGON ((581275.000 184964.000, 581431.000 18... 9436 9.875395e+05

68957 rows × 6 columns

with_names = signatures_urban[signatures_urban.settlement.isin(joined.settlement)]

with_names.settlement.nunique()

1206

no_names = signatures_urban[~signatures_urban.settlement.isin(joined.settlement)]

no_names.settlement

10883       1
10884       1
10885       2
10887       4
10889       6
         ... 
96594    9432
96647    9433
96648    9434
96658    9435
96691    9436
Name: settlement, Length: 10436, dtype: int32

grouper = with_names.groupby(["settlement", "type"])
areas = grouper["area"].sum().unstack().fillna(0)

areas_sorted = areas.sort_values(order, ascending=False)[order]

joined
local_type geometry name1 index_right settlement
2023027 Suburban Area POINT (418095.000 185406.000) Nythe 73480 1021
2049081 Suburban Area POINT (417576.000 184893.000) Park North 73480 1021
2023028 Suburban Area POINT (292127.000 90785.000) Marsh Barton 46382 285
2023029 Suburban Area POINT (295991.000 91247.000) Clyst Heath 23788 285
2040633 Suburban Area POINT (294268.000 91766.000) St Loye's 23788 285
... ... ... ... ... ...
2287377 Suburban Area POINT (430095.000 581544.000) Malvin's Close 30755 1006
2288413 Other Settlement POINT (395461.000 395566.000) The Railway Sidings 87974 407
2288557 Other Settlement POINT (206144.000 67004.000) Town End 21978 25
2288563 Other Settlement POINT (465287.000 103797.000) Vicarage Row 58015 1508
2288564 Other Settlement POINT (465332.000 103793.000) St Francis Court 58015 1508

10561 rows × 5 columns

plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.iloc[:200].T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='type'>
../_images/settlment_delin_33_1.png 
plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm())

<Axes: xlabel='settlement', ylabel='type'>
../_images/settlment_delin_34_1.png 
plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm(), cmap="Blues")

<Axes: xlabel='settlement', ylabel='type'>
../_images/settlment_delin_35_1.png 
import urbangrammar_graphics as ugg
from matplotlib.colors import LinearSegmentedColormap

ugg.COLORS

[(0.19921875, 0.203125, 0.1953125),
 (0.23046875, 0.4296875, 0.55078125),
 (0.73828125, 0.35546875, 0.30859375),
 (0.5625, 0.640625, 0.4921875),
 (0.9375, 0.78125, 0.34375),
 (0.58203125, 0.3984375, 0.4296875)]

cmap = LinearSegmentedColormap.from_list("ugg", [ugg.COLORS[4], ugg.COLORS[3]])
cmap
ugg
ugg colormap
under
bad
over 
areas_sorted
type Hyper concentrated urbanity Concentrated urbanity Metropolitan urbanity Regional urbanity Local urbanity Dense urban neighbourhoods Gridded residential quarters Dense residential neighbourhoods Connected residential neighbourhoods Accessible suburbia Disconnected suburbia Open sprawl Warehouse/Park land
settlement
1845 1.306332e+06 7.884144e+06 1.321113e+07 3.960246e+07 1.155779e+08 2.201192e+08 6.699935e+06 2.235081e+08 1.364017e+08 2.132323e+08 8.389185e+07 2.989552e+08 3.819127e+08
876 0.000000e+00 0.000000e+00 1.075760e+06 2.806010e+06 5.745378e+06 2.758493e+07 1.721331e+05 4.842811e+07 4.149977e+07 6.251046e+07 5.417000e+07 1.447718e+08 3.386026e+08
389 0.000000e+00 0.000000e+00 6.359560e+05 7.521293e+06 7.460783e+06 9.309406e+06 1.394896e+06 2.096403e+07 1.544039e+07 2.041219e+07 8.596605e+05 3.194950e+07 1.814534e+07
89 0.000000e+00 0.000000e+00 6.071265e+05 6.881265e+06 1.262601e+07 1.710456e+07 1.253209e+06 3.929345e+07 4.141944e+07 7.314527e+07 3.987755e+06 1.679558e+08 6.047482e+07
407 0.000000e+00 0.000000e+00 5.156702e+05 3.950296e+06 7.917305e+06 5.427237e+07 3.914361e+07 1.443043e+08 8.382661e+07 1.885914e+08 3.735568e+07 5.668694e+08 2.810701e+08
... ... ... ... ... ... ... ... ... ... ... ... ... ...
8150 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.662141e+04
7967 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.577238e+04
9059 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.555250e+04
8161 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.293481e+04
8136 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.137823e+03

1206 rows × 13 columns

plt.subplots(figsize=(32, 8))
sns.heatmap(data=areas_sorted.T, norm=LogNorm(), cmap="cividis_r")

<Axes: xlabel='settlement', ylabel='type'>
../_images/settlment_delin_40_1.png 
placenames
local_type geometry name1
2023027 Suburban Area POINT (418095.000 185406.000) Nythe
2023028 Suburban Area POINT (292127.000 90785.000) Marsh Barton
2023029 Suburban Area POINT (295991.000 91247.000) Clyst Heath
2023030 Suburban Area POINT (233883.000 84586.000) Kensey Valley Meadow
2023031 Suburban Area POINT (293538.000 93365.000) Polsloe
... ... ... ...
2289904 Other Settlement POINT (199430.000 907051.000) Garadheancal
2289905 Other Settlement POINT (636776.000 151377.000) Wingfield Place
2289906 Suburban Area POINT (480934.000 263680.000) Blackthorn
2289907 Hamlet POINT (140772.000 799096.000) Raonapoll
2289908 Suburban Area POINT (249437.000 628134.000) Hillhead Heights

43209 rows × 3 columns

with_names
id code type geometry settlement area
10882 10882_ACS ACS Accessible suburbia POLYGON ((143345.668 932691.413, 143345.418 93... 0 9.843275e+04
10886 10886_ACS ACS Accessible suburbia POLYGON ((164091.320 40526.696, 164093.601 405... 3 1.941262e+05
10888 10888_ACS ACS Accessible suburbia POLYGON ((167653.791 43194.946, 167654.353 431... 5 9.879056e+03
10893 10893_ACS ACS Accessible suburbia POLYGON ((170174.747 41387.203, 170172.782 413... 8 1.610704e+05
10895 10895_ACS ACS Accessible suburbia POLYGON ((175356.122 54184.967, 175365.952 541... 10 6.876061e+03
... ... ... ... ... ... ...
96686 96686_HDU HDU Hyper concentrated urbanity POLYGON ((528530.170 181095.720, 528544.720 18... 1845 2.823163e+04
96687 96687_HDU HDU Hyper concentrated urbanity POLYGON ((528602.732 181135.450, 528572.630 18... 1845 1.797907e+03
96688 96688_HDU HDU Hyper concentrated urbanity POLYGON ((528643.907 181217.443, 528638.400 18... 1845 1.271663e+06
96689 96689_HDU HDU Hyper concentrated urbanity POLYGON ((528734.582 181316.822, 528732.783 18... 1845 1.872188e+03
96690 96690_HDU HDU Hyper concentrated urbanity POLYGON ((530336.433 181039.755, 530336.117 18... 1845 2.767724e+03

58521 rows × 6 columns

placenames.local_type.unique()

array(['Suburban Area', 'Hamlet', 'Village', 'Other Settlement', 'Town',
       'City'], dtype=object)

with_names.groupby("settlement")["type"].nunique().value_counts() / with_names.settlement.nunique()

type
2     0.285240
3     0.173300
1     0.145108
4     0.117745
5     0.068823
6     0.064677
7     0.050580
8     0.040630
9     0.033167
10    0.011609
11    0.008292
13    0.000829
Name: count, dtype: float64

%%time
settlement_boundaries = with_names[["geometry", "settlement"]].dissolve("settlement")

CPU times: user 1min 28s, sys: 33.3 ms, total: 1min 28s
Wall time: 1min 28s

names = []
for poly in settlement_boundaries.geometry:
    names_in = placenames.iloc[placenames.sindex.query(poly, predicate="contains")]
    if names_in.shape[0] == 1:
        names.append(names_in.name1.iloc[0])
    elif (names_in.local_type == "City").any():
        names.append(names_in[names_in.local_type == "City"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Town").any():
        names.append(names_in[names_in.local_type == "Town"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Suburban Area").any():
        names.append(names_in[names_in.local_type == "Suburban Area"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Village").any():
        names.append(names_in[names_in.local_type == "Village"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Hamlet").any():
        names.append(names_in[names_in.local_type == "Hamlet"].name1.str.cat(sep=' - '))
    elif (names_in.local_type == "Other Settlement").any():
        names.append(names_in[names_in.local_type == "Other Settlement"].name1.str.cat(sep=' - '))
    else:
        names.append(None)

settlement_boundaries["name"] = names

settlement_boundaries
geometry name
settlement
0 POLYGON ((142667.472 932126.244, 142647.194 93... Stornoway
3 POLYGON ((163950.904 40031.623, 163950.405 400... Camborne
5 MULTIPOLYGON (((166904.881 42824.079, 166903.3... Illogan
8 POLYGON ((169788.398 41575.672, 169777.856 415... Redruth
10 POLYGON ((175581.974 54311.766, 175582.008 543... Perranporth
... ... ...
8968 POLYGON ((501071.199 203479.014, 501071.401 20... Bovingdon
8996 POLYGON ((504988.264 302841.508, 504992.439 30... Wittering
9059 POLYGON ((515161.410 416170.202, 515161.543 41... None
9230 POLYGON ((560071.598 165353.771, 560073.233 16... New Ash Green
9273 POLYGON ((584270.281 332038.541, 584270.333 33... Wicken Green Village

1206 rows × 2 columns

settlement_boundaries = settlement_boundaries.join(areas_sorted)

settlement_boundaries.loc[areas_sorted.index, "rank"] = range(len(settlement_boundaries))

def get_level(row):
    for i, val in enumerate(row, 1):
        if val > 0:
            return i

settlement_boundaries["level"] = settlement_boundaries.iloc[:, 3:-1].apply(get_level, axis=1)

settlement_boundaries.sort_values('rank')
geometry name area Hyper concentrated urbanity Concentrated urbanity Metropolitan urbanity Regional urbanity Local urbanity Dense urban neighbourhoods Gridded residential quarters Dense residential neighbourhoods Connected residential neighbourhoods Accessible suburbia Disconnected suburbia Open sprawl Warehouse/Park land rank level
settlement
1845 MULTIPOLYGON (((501683.158 157433.012, 501682.... City of Westminster - London - City of London 1742.303086 1.306332e+06 7.884144e+06 1.321113e+07 3.960246e+07 1.155779e+08 2.201192e+08 6.699935e+06 2.235081e+08 1.364017e+08 2.132323e+08 8.389185e+07 2.989552e+08 3.819127e+08 0.0 1
876 MULTIPOLYGON (((389083.539 282798.245, 389082.... Birmingham - Wolverhampton 727.366923 0.000000e+00 0.000000e+00 1.075760e+06 2.806010e+06 5.745378e+06 2.758493e+07 1.721331e+05 4.842811e+07 4.149977e+07 6.251046e+07 5.417000e+07 1.447718e+08 3.386026e+08 1.0 3
389 MULTIPOLYGON (((316868.400 667252.163, 316862.... Edinburgh 134.093444 0.000000e+00 0.000000e+00 6.359560e+05 7.521293e+06 7.460783e+06 9.309406e+06 1.394896e+06 2.096403e+07 1.544039e+07 2.041219e+07 8.596605e+05 3.194950e+07 1.814534e+07 2.0 3
89 MULTIPOLYGON (((248116.901 660962.889, 248117.... Glasgow 424.748668 0.000000e+00 0.000000e+00 6.071265e+05 6.881265e+06 1.262601e+07 1.710456e+07 1.253209e+06 3.929345e+07 4.141944e+07 7.314527e+07 3.987755e+06 1.679558e+08 6.047482e+07 3.0 3
407 MULTIPOLYGON (((329043.772 386171.162, 329042.... Liverpool - Manchester - Salford 1407.816751 0.000000e+00 0.000000e+00 5.156702e+05 3.950296e+06 7.917305e+06 5.427237e+07 3.914361e+07 1.443043e+08 8.382661e+07 1.885914e+08 3.735568e+07 5.668694e+08 2.810701e+08 4.0 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8150 POLYGON ((312936.898 211049.775, 312936.408 21... None 0.026621 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.662141e+04 1201.0 13
7967 POLYGON ((224900.071 675436.621, 224900.547 67... None 0.025772 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.577238e+04 1202.0 13
9059 POLYGON ((515161.410 416170.202, 515161.543 41... None 0.015553 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.555250e+04 1203.0 13
8161 POLYGON ((314695.924 202755.247, 314695.871 20... None 0.012935 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.293481e+04 1204.0 13
8136 POLYGON ((308157.203 194986.868, 308157.930 19... Abercynon 0.002138 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.137823e+03 1205.0 13

1206 rows × 18 columns

settlement_boundaries.to_parquet("./settlement_boundaries.parquet")

/opt/conda/envs/gds/lib/python3.9/site-packages/pyarrow/pandas_compat.py:373: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.
  if _pandas_api.is_sparse(col):

settlement_boundaries["area"] = settlement_boundaries.area / 1000000

fua = gpd.read_parquet("../../urbangrammar_samba/spatial_signatures/esda/fua.pq")
ttwa = gpd.read_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa.pq")

m = settlement_boundaries.set_geometry(settlement_boundaries.simplify(100)).explore(tiles="CartoDB positron")
fua.explore(m=m, color='red')
Make this Notebook Trusted to load map: File -> Trust Notebook
bds_plot = settlement_boundaries.set_geometry(settlement_boundaries.simplify(100)).to_crs(3857)
fua_plot = fua.to_crs(3857)

ttwa.explore(tiles="cartodb voyager")
Make this Notebook Trusted to load map: File -> Trust Notebook
tiles = cx.providers.CartoDB.Positron
tiles_labels = cx.providers.CartoDB.PositronOnlyLabels

from matplotlib.patches import Patch

ax = bds_plot.plot(color=ugg.COLORS[1], figsize=(10, 10), zorder=10, alpha=.8)
# bds_plot.boundary.plot(ax=ax, color=ugg.COLORS[0], linewidth=.75)
fua_plot.plot(ax=ax, alpha=.7, color=ugg.COLORS[4])
# fua_plot.boundary.plot(ax=ax, alpha=1, color=ugg.COLORS[4], linewidth=1)
fua_plot.boundary.plot(ax=ax, alpha=1, color=ugg.COLORS[0], linewidth=.8)
ax.set_ylim(6400000, 7250000)
ax.set_xlim(-650000, 220000)
ax.set_axis_off()
cx.add_basemap(ax=ax, source=tiles.build_url(scale_factor="@2x"), attribution=tiles.attribution)

legend_elements = [
    Patch(facecolor=ugg.COLORS[1], label='Settlement area'),
    Patch(facecolor=ugg.COLORS[4], alpha=.7, edgecolor=ugg.COLORS[0], linewidth=.8, label='Functional urban area'),
]
ax.legend(handles=legend_elements, loc='upper right', frameon=False)

scalebar = ScaleBar(dx=1,
                    color=ugg.COLORS[0],
                    location='lower right',
                    height_fraction=0.002,
                    pad=.5,
                    frameon=False,
                    )
ax.add_artist(scalebar)

plt.savefig("figs/overlay_set_fua.png", dpi=144, bbox_inches="tight")
../_images/settlment_delin_64_0.png 
ax = bds_plot.plot(color=ugg.COLORS[1], figsize=(10, 10), zorder=10, alpha=.8)
bds_plot.boundary.plot(ax=ax, color=ugg.COLORS[0], linewidth=.75)
# fua_plot.plot(ax=ax, alpha=.7, color=ugg.COLORS[4])
ax.set_ylim(7470000, 7600000)
ax.set_xlim(-550000, -300000)
ax.set_axis_off()
cx.add_basemap(ax=ax, source=tiles.build_url(scale_factor="@2x"), attribution=tiles.attribution)

legend_elements = [
    Patch(facecolor=ugg.COLORS[1], label='Settlement area'),
]
ax.legend(handles=legend_elements, loc='upper left', frameon=True)

scalebar = ScaleBar(dx=1,
                    color=ugg.COLORS[0],
                    location='lower right',
                    height_fraction=0.002,
                    pad=.5,
                    frameon=False,
                    )
ax.add_artist(scalebar)

plt.savefig("figs/settlements_scotland.png", dpi=144, bbox_inches="tight")
../_images/settlment_delin_65_0.png 
ugg.CMAP
from_list
from_list colormap
under
bad
over 
fua.area.max() / 1000000

6605.0

settlement_boundaries
geometry name area
settlement
0 POLYGON Z ((142667.472 932126.244 0.000, 14264... Stornoway 1.726063
3 POLYGON Z ((163950.904 40031.623 0.000, 163950... Camborne 3.282427
5 POLYGON Z ((167036.075 42671.447 0.000, 167034... Illogan 0.593929
8 POLYGON Z ((169788.398 41575.672 0.000, 169777... Redruth 2.543934
10 POLYGON Z ((175581.974 54311.766 0.000, 175582... Perranporth 0.383067
... ... ... ...
9497 POLYGON Z ((501071.199 203479.014 0.000, 50107... Bovingdon 0.298797
9526 POLYGON Z ((504988.264 302841.508 0.000, 50499... Wittering 0.444567
9592 POLYGON Z ((515161.410 416170.202 0.000, 51516... None 0.015553
9777 POLYGON Z ((560071.598 165353.771 0.000, 56007... New Ash Green 0.644549
9822 POLYGON Z ((584270.281 332038.541 0.000, 58427... Wicken Green Village 0.066374

1208 rows × 3 columns

(settlement_boundaries['area'] < 1).sum()

584

settlement_boundaries.loc[[5271]].explore()
Make this Notebook Trusted to load map: File -> Trust Notebook
areas_sorted["name"] = settlement_boundaries["name"]

plot_data = areas_sorted.set_index("name", drop=True)

plot_data.index.name = "Settlement"
plot_data.columns.name = "Signature type"

plt.subplots(figsize=(16, 4))
ax = sns.heatmap(data=plot_data.T, norm=LogNorm(), cmap="cividis_r")
plt.tick_params(
    axis='x',     
    which='both', 
    bottom=False,     
    top=False,         
    labelbottom=False)
plt.savefig("hierarchy.pdf")
../_images/settlment_delin_74_0.png 
plot_data.index[12:20]

Index(['Plymouth', 'Sunderland - Newcastle upon Tyne', 'Aberdeen',
       'Nottingham', 'Norwich', 'Scarborough',
       'Skelton - Saltburn-by-the-Sea - Brotton - Marske-by-the-Sea - Redcar - Yarm - Thornaby-on-Tees - Stockton-on-Tees - Billingham - Middlesbrough - Hartlepool',
       'Abertawe'],
      dtype='object', name='Settlement')

plot_data.index[1040:1045]

Index(['Coundon', 'New Ash Green', 'Hamble-le-Rice', None, 'Long Buckby'], dtype='object', name='Settlement')

settlement_sig_area = with_names.groupby(["settlement", "signature_type"])["area"].sum()

settlement_sig_area.xs("Accessible suburbia", level=1)

settlement
0       9.843275e+04
3       1.941262e+05
5       9.879056e+03
8       1.610704e+05
10      6.876061e+03
            ...     
2759    2.782918e+05
2760    7.113730e+05
2761    3.394846e+06
2762    7.550956e+06
2765    2.403334e+04
Name: area, Length: 857, dtype: float64

settlement_boundaries.assign(sig_area=settlement_sig_area.xs("Gridded residential quarters", level=1) / settlement_boundaries.area).plot("sig_area", figsize=(20, 20), scheme="quantiles", k=10s).set_axis_off()
../_images/settlment_delin_79_0.png 
settlement_boundaries.assign(sig_area=settlement_sig_area.xs("Dense urban neighbourhoods", level=1) / settlement_boundaries.area).plot("sig_area", figsize=(20, 20), scheme="quantiles", k=10).set_axis_off()
../_images/settlment_delin_80_0.png 
with_names.groupby(["signature_type"])["area"].sum().sort_values() / 1000000

signature_type
Hyper concentrated urbanity                1.306332
Concentrated urbanity                      7.884144
Metropolitan urbanity                     16.523881
Regional urbanity                         76.432434
Local urbanity                           231.114682
Gridded residential quarters             260.548485
Connected residential neighbourhoods     564.556224
Dense urban neighbourhoods               570.643221
Disconnected suburbia                    698.800932
Dense residential neighbourhoods         956.964941
Accessible suburbia                     2194.269106
Warehouse/Park land                     2441.457800
Open sprawl                             4968.897217
Name: area, dtype: float64